Project Summary/Abstract At a time when cancer research is increasingly complex and expensive to conduct, cost-effective study design is an imperative. Despite the well-known and long-standing problem of depending on unreliable estimates of the intraclass correlation (ICC) when designing studies, serious efforts to address this problem are lacking. The objective of the proposed research is to develop novel methods of predicting the ICC using mechanistic models of within-cluster dynamics and to incorporate these methods into sample size and power calculations. Cancer prevention studies motivate the work. The proposed research will have widespread practical applications for more cost-effective and scientifically defensible study designs. PUBLIC HEALTH RELEVANCE: Public Health Relevance When planning a cancer research study, good design is needed to ensure that the study will produce the data necessary to answer important research questions while minimizing cost. The proposed research will develop new methods of study design for a common data scenario, clustered binary data, thereby reducing costs and increasing the likelihood that research questions will be answered conclusively.